BET_VH: a probabilistic tool for long-term volcanic hazard assessment

In this paper, we illustrate a Bayesian Event Tree to estimate Volcanic Hazard (BET_VH). The procedure enables us to calculate the probability of any kind of long-term hazardous event for which we are interested, accounting for the intrinsic stochastic nature of volcanic eruptions and our limited knowledge regarding related processes. For the input, the code incorporates results from numerical models simulating the impact of hazardous volcanic phenomena on an area and data from the eruptive history. For the output, the code provides a wide and exhaustive set of spatiotemporal probabilities of different events; these probabilities are estimated by means of a Bayesian approach that allows all uncertainties to be properly accounted for. The code is able to deal with many eruptive settings simultaneously, weighting each with its own probability of occurrence. In a companion paper, we give a detailed example of application of this tool to the Campi Flegrei caldera, in order to estimate the hazard from tephra fall. © The Author(s) 2010

S43A-2470: On the uncertainties of seismic parameters: a Bayesian framework for their estimation using Brune's model

The estimation of seismic parameters from ground-motion records is subject to many uncertainties, such as: (i) parameterization, modeling procedures and underlying hypotheses, (ii) approximated input parameters, (iii) instrumental errors on records and their impact in data post-processing, (iv) procedures to estimate model’s parameters. However these uncertainties are rarely treated and propagated to the final results. For example, on one side, density of rocks, velocity model, geometrical spreading, radiation pattern are just some of the common parameters needed to estimate the main seismic parameters of an earthquake and are generally used as average values. On the other side, uncertainties derived from the acquisition system and processing of the data are often neglected. Nevertheless, in many cases these uncertainties may be particularly important, as for example in the analysis of historical earthquakes, where both instrumental response and treatment of analog records intrinsically imply non negligible sources of uncertainty. Here, we present a new Bayesian procedure to estimate seismic parameters that allows: (i) to obtain a robust estimation of the Brune’s model parameters (Brune 1970, 1971) and relatives uncertainties, (ii) to account for the uncertainty related to the Earth model, and (iii) to propagate such uncertainties on the estimation of seismological parameters (seismic moment, moment magnitude, radius of the circular source zone and static stress drop). It is important to highlight that this study does not want to discuss the validity or the physical significance of the Brune’s model, but it is focused on the details of how to fit it on a dataset in order to evaluate the seismological parameters, accounting and properly propagating a rather large range of uncertainties. These capabilities of the proposed procedure are finally demonstrated through an illustrative application analyzing seismic records from historical events

A simple two-state model interprets temporal modulations in eruptive activity and enhances multivolcano hazard quantification

Volcanic activity typically switches between high-activity states with many eruptions and low-activity states with few or no eruptions. We present a simple two-regime physics-informed statistical model that allows interpreting temporal modulations in eruptive activity. The model enhances comprehension and comparison of different volcanic systems and enables homogeneous integration into multivolcano hazard assessments that account for potential changes in volcanic regimes. The model satisfactorily fits the eruptive history of the three active volcanoes in the Neapolitan area, Italy (Mt. Vesuvius, Campi Flegrei, and Ischia) which encompass a wide range of volcanic behaviors. We find that these volcanoes have appreciably different processes for triggering and ending high-activity periods connected to different dominant volcanic processes controlling their eruptive activity, with different characteristic times and activity rates (expressed as number of eruptions per time interval). Presently, all three volcanoes are judged to be in a low-activity state, with decreasing probability of eruptions for Mt. Vesuvius, Ischia, and Campi Flegrei, respectively

A global probabilistic tsunami hazard assessment from earthquake sources

Large tsunamis occur infrequently but have the capacity to cause enormous numbers of casualties, damage to the built environment and critical infrastructure, and economic losses. A sound understanding of tsunami hazard is required to underpin management of these risks, and while tsunami hazard assessments are typically conducted at regional or local scales, globally consistent assessments are required to support international disaster risk reduction efforts, and can serve as a reference for local and regional studies. This study presents a global-scale probabilistic tsunami hazard assessment (PTHA), extending previous global-scale assessments based largely on scenario analysis. Only earthquake sources are considered, as they represent about 80% of the recorded damaging tsunami events. Globally extensive estimates of tsunami run-up height are derived at various exceedance rates, and the associated uncertainties are quantified. Epistemic uncertainties in the exceedance rates of large earthquakes often lead to large uncertainties in tsunami run-up. Deviations between modelled tsunami run-up and event observations are quantified, and found to be larger than suggested in previous studies. Accounting for these deviations in PTHA is important, as it leads to a pronounced increase in predicted tsunami run-up for a given exceedance rate.Published219-2446T. Studi di pericolosità sismica e da maremot

VIGIL: a Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling

Probabilistic volcanic hazard assessment is a standard methodology based on running a deterministic hazard quantification tool multiple times to explore the full range of uncertainty in the input parameters and boundary conditions, in order to probabilistically quantify the variability of outputs accounting for such uncertainties. Nowadays, different volcanic hazards are quantified by means of this approach. Among these, volcanic gas emission is particularly relevant given the threat posed to human health if concentrations and exposure times exceed certain thresholds. There are different types of gas emissions but two main scenarios can be recognized: hot buoyant gas emissions from fumaroles and the ground and dense gas emissions feeding density currents that can occur, e.g., in limnic eruptions. Simulation tools are available to model the evolution of critical gas concentrations over an area of interest. Moreover, in order to perform probabilistic hazard assessments of volcanic gases, simulations should account for the natural variability associated to aspects such as seasonal and daily wind conditions, localized or diffuse source locations, and gas fluxes. Here we present VIGIL (automatized probabilistic VolcanIc Gas dIspersion modeLling), a new Python tool designed for managing the entire simulation workflow involved in single and probabilistic applications of gas dispersion modelling. VIGIL is able to manage the whole process from meteorological data processing, needed to run gas dispersion in both the dilute and dense gas flow scenarios, to the post processing of models’ outputs. Two application examples are presented to show some of the modelling capabilities offered by VIGIL

A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

Volcanic water-sediment flows, commonly known as lahars, can often pose a higher threat to population and infrastructure than primary volcanic hazardous processes such as tephra fallout and Pyroclastic Density Currents (PDCs). Lahars are volcaniclastic flows of water, volcanic debris and entrained sediments that can travel long distances from their source, causing severe damage by impact and burial. Lahars are frequently triggered by intense or prolonged rainfall occurring after explosive eruptions, and their occurrence depends on numerous factors including the spatio-temporal rainfall characteristics, the spatial distribution and hydraulic properties of the tephra deposit, and the pre- and post-eruption topography. Modeling (and forecasting) such a complex system requires the quantification of aleatory variability in the lahar triggering and propagation. To fulfill this goal, we develop a novel framework for probabilistic hazard assessment of lahars within a multi-hazard environment, based on coupling a versatile probabilistic model for lahar triggering (a Bayesian Belief Network: Multihaz) with a dynamic physical model for lahar propagation (LaharFlow). Multihaz allows us to estimate the probability of lahars of different volumes occurring by merging varied information about regional rainfall, scientific knowledge on lahar triggering mechanisms and, crucially, probabilistic assessment of available pyroclastic material from tephra fallout and PDCs. LaharFlow propagates the aleatory variability modeled by Multihaz into hazard footprints of lahars. We apply our framework to Somma-Vesuvius (Italy) because: (1) the volcano is strongly lahar-prone based on its previous activity, (2) there are many possible source areas for lahars, and (3) there is high density of population nearby. Our results indicate that the size of the eruption preceding the lahar occurrence and the spatial distribution of tephra accumulation have a paramount role in the lahar initiation and potential impact. For instance, lahars with initiation volume ≥105 m3 along the volcano flanks are almost 60% probable to occur after large-sized eruptions (~VEI ≥ 5) but 40% after medium-sized eruptions (~VEI4). Some simulated lahars can propagate for 15 km or reach combined flow depths of 2 m and speeds of 5–10 m/s, even over flat terrain. Probabilistic multi-hazard frameworks like the one presented here can be invaluable for volcanic hazard assessment worldwide

Testing gas dispersion modelling: a case study at La Soufrière volcano (Guadeloupe, Lesser Antilles)

Volcanic gas dispersal can be a serious threat to people living near active volcanoes since it can have short- and long-term effects on human health, and severely damage crops and agricultural land. In recent decades, reliable computational models have significantly advanced, and now they may represent a valuable tool to make quantitative and testable predictions, supporting gas dispersal forecasting and hazard assessments for public safety. Before applying a specific modelling tool into hazard quantification, its calibration and its sensitivity to initial and boundary conditions should be carefully tested against available data, in order to produce unbiased hazard quantifications. In this study, we provided a number of prototypical tests aimed to validate the modelling of gas dispersal from a hazard perspective. The tests were carried out at La Soufrière de Guadeloupe volcano, one of the most active gas emitters in the Lesser Antilles. La Soufrière de Guadeloupe has shown quasi-permanent degassing of a low-temperature hydrothermal nature since its last magmatic eruption in 1530 CE, when the current dome was emplaced. We focused on the distribution of CO2 and H2S discharged from the three main present-day fumarolic sources at the summit, using the measurements of continuous gas concentrations collected in the period March–April 2017. We developed a new probabilistic implementation of the Eulerian code DISGAS-2.0 for passive gas dispersion coupled with the mass-consistent Diagnostic Wind Model, using local wind measurements and atmospheric stability information from a local meteorological station and ERA5 reanalysis data. We found that model outputs were not significantly affected by the type of wind data but rather upon the relative positions of fumaroles and measurement stations. Our results reproduced the statistical variability in daily averages of observed data over the investigated period within acceptable ranges, indicating the potential usefulness of DISGAS-2.0 as a tool for reproducing the observed fumarolic degassing and for quantifying gas hazard at La Soufrière. The adopted testing procedure allows for an aware application of simulation tools for quantifying the hazard, and thus we think that this kind of testing should actually be the first logical step to be taken when applying a simulator to assess (gas) hazard in any other volcanic contexts

Multisource Bayesian Probabilistic Tsunami Hazard Analysis for the Gulf of Naples (Italy)

A methodology for a comprehensive probabilistic tsunami hazard analysis is presented for the major sources of tsunamis (seismic events, landslides, and volcanic activity) and preliminarily applied in the Gulf of Naples (Italy). The methodology uses both a modular procedure to evaluate the tsunami hazard and a Bayesian analysis to include the historical information of the past tsunami events. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0001 the submarine earthquakes and the submarine mass failures are initially identified in a gridded domain and defined by a set of parameters, producing the sea floor deformations and the corresponding initial tsunami waves. Differently volcanic tsunamis generate sea surface waves caused by pyroclastic density currents from Somma‐Vesuvius. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0002 the tsunami waves are simulated and propagated in the deep sea by a numerical model that solves the shallow water equations. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0003 the tsunami wave heights are estimated at the coast using the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0004's amplification law. The selected tsunami intensity is the wave height. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0005 the probabilistic tsunami analysis computes the long‐term comprehensive Bayesian probabilistic tsunami hazard analysis. In the prior analysis the probabilities from the scenarios in which the tsunami parameter overcomes the selected threshold levels are combined with the spatial, temporal, and frequency‐size probabilities of occurrence of the tsunamigenic sources. The urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0006 probability density functions are integrated with the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0007 derived from the historical information based on past tsunami data. The urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0008 probability density functions are evaluated to produce the hazard curves in selected sites of the Gulf of Naples. Plain Language Summary Probabilistic analyses are essential to estimate the natural hazards caused by infrequent and devastating events and to elaborate risk assessments aiming to mitigate and reduce the impact of the natural disasters on society. Probabilistic tsunami hazard analyses use procedures that trace and weight the different tsunami sources (submarine earthquakes, aerial/submarine slides, volcanic activity, meteorological events, and asteroid impacts) with varying probability of occurrence. The scope of the present methodology is the reduction of possible biases and underestimations that arise by focusing on a single tunamigenic source. The multisource probabilistic tsunami hazard analysis is applied to a real case study, the Gulf of Naples (Italy), where relevant threats due to natural events exist in a high densely populated district. The probabilistic hazard procedure takes into account multiple tsunamigenic sources in the region and provides a first‐order prioritization of the different sources in a long‐term comprehensive analysis. The methodology is based on a Bayesian approach that merges computational hazard quantification (based on source‐tsunami simulations) and past data, appropriately including in the quantification the epistemic uncertainty. For the first time a probabilistic analysis of the tsunami hazard in the region is presented taking into consideration multiple tsunamigenic sources

Review of multiple hazards in volcanic islands to enable the management of long-term risks: the cases of Ischia and Vulcano, Italy

The management of long-term volcanic risks represents a challenge that requires a close cooperation between science and decision-making. This is particularly crucial in volcanic islands, which are characterized by multiple hazards concentrated in a relatively small environment, often associated with a large seasonality of exposure due to tourism. The scientific challenges are mainly the quantification and the characterization of the interactions among the multiple hazardous phenomena that may occur during the different “states of thevolcano” (quiescence, unrest, eruption) and the definition of robust methods to forecast the transition between these states. For these topics, the emerging scientific knowledge is often rather limited and uncertain and, also in case it was well constrained, difficult to communicate to decision makers due to its intrinsic complexity. On the other side, the challenge for decision making is to assimilate this uncertain knowledgeand translate it into actions. Here, we discuss the experience gained in two working groups (WGs) in charge of reviewing the state of knowledge about volcanic hazards for the Italian volcanic islands of Ischia and Vulcano to build the scientific ground for subsequent decision making. These WGs, formed within the agreement between INGV and the Italian Civil Protection Department, involved about 20 researchers from INGV and Universities, as well as representatives of the Italian Civil Protection, to facilitate the reciprocal understanding and to address the work toward useful results for decision making. The WGs reviewed all the potential volcanic hazards for Ischia and Vulcano based on literature, results of previous projects, as well as ad hoc audits of other experts on specific topics, and organized a workshop to present the results and receive feedbacks from the extended scientific community

Probabilistic tsunami forecasting for early warning

Tsunami warning centres face the challenging task of rapidly forecasting tsunami threat immediately after an earthquake, when there is high uncertainty due to data deficiency. Here we introduce Probabilistic Tsunami Forecasting (PTF) for tsunami early warning. PTF expli- citly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs- false-alarms. Impact forecasts and resulting recommendations become progressively less uncertain as new data become available. Here we report an implementation for near-source early warning and test it systematically by hindcasting the great 2010 M8.8 Maule (Chile) and the well-studied 2003 M6.8 Zemmouri-Boumerdes (Algeria) tsunamis, as well as all the Mediterranean earthquakes that triggered alert messages at the Italian Tsunami Warning Centre since its inception in 2015, demonstrating forecasting accuracy over a wide range of magnitudes and earthquake types